Overview

Dataset statistics

Number of variables19
Number of observations6720
Missing cells11244
Missing cells (%)8.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory997.6 KiB
Average record size in memory152.0 B

Variable types

Categorical8
Numeric11

Warnings

mifid_money_other_brokers is highly correlated with mifid_invested_other_brokersHigh correlation
mifid_invested_other_brokers is highly correlated with mifid_money_other_brokersHigh correlation
finish_mifid_days has 2900 (43.2%) missing values Missing
first_deposit_days has 4733 (70.4%) missing values Missing
first_trade_investor_account_demo_days has 3608 (53.7%) missing values Missing
start_mifid_days has 4844 (72.1%) zeros Zeros
finish_mifid_days has 801 (11.9%) zeros Zeros
first_deposit_days has 89 (1.3%) zeros Zeros
first_deposit_amount has 4733 (70.4%) zeros Zeros
first_deposit_platform has 729 (10.8%) zeros Zeros
mifid_actual_savings has 650 (9.7%) zeros Zeros
mifid_next_year_savings has 650 (9.7%) zeros Zeros
mifid_invested_other_brokers has 3593 (53.5%) zeros Zeros
first_trade_investor_account_demo_days has 1807 (26.9%) zeros Zeros
days_until_conversion_or_today has 70 (1.0%) zeros Zeros

Reproduction

Analysis started2021-06-02 17:18:10.742672
Analysis finished2021-06-02 17:19:04.676566
Duration53.93 seconds
Software versionpandas-profiling v2.13.0
Download configurationconfig.yaml

Variables

user_currency
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size52.6 KiB
USD
3279 
EUR
3121 
GBP
 
318
NO_CURRENCY
 
2

Length

Max length11
Median length3
Mean length3.002380952
Min length3

Characters and Unicode

Total characters20176
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEUR
2nd rowUSD
3rd rowGBP
4th rowUSD
5th rowUSD
ValueCountFrequency (%)
USD3279
48.8%
EUR3121
46.4%
GBP318
 
4.7%
NO_CURRENCY2
 
< 0.1%
2021-06-02T19:19:05.186678image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-06-02T19:19:05.398648image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
usd3279
48.8%
eur3121
46.4%
gbp318
 
4.7%
no_currency2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
U6402
31.7%
S3279
16.3%
D3279
16.3%
R3125
15.5%
E3123
15.5%
G318
 
1.6%
B318
 
1.6%
P318
 
1.6%
N4
 
< 0.1%
C4
 
< 0.1%
Other values (3)6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter20174
> 99.9%
Connector Punctuation2
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
U6402
31.7%
S3279
16.3%
D3279
16.3%
R3125
15.5%
E3123
15.5%
G318
 
1.6%
B318
 
1.6%
P318
 
1.6%
N4
 
< 0.1%
C4
 
< 0.1%
Other values (2)4
 
< 0.1%
ValueCountFrequency (%)
_2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin20174
> 99.9%
Common2
 
< 0.1%

Most frequent character per script

ValueCountFrequency (%)
U6402
31.7%
S3279
16.3%
D3279
16.3%
R3125
15.5%
E3123
15.5%
G318
 
1.6%
B318
 
1.6%
P318
 
1.6%
N4
 
< 0.1%
C4
 
< 0.1%
Other values (2)4
 
< 0.1%
ValueCountFrequency (%)
_2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII20176
100.0%

Most frequent character per block

ValueCountFrequency (%)
U6402
31.7%
S3279
16.3%
D3279
16.3%
R3125
15.5%
E3123
15.5%
G318
 
1.6%
B318
 
1.6%
P318
 
1.6%
N4
 
< 0.1%
C4
 
< 0.1%
Other values (3)6
 
< 0.1%

user_country
Real number (ℝ≥0)

Distinct122
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.54985119
Minimum0
Maximum121
Zeros11
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size52.6 KiB
2021-06-02T19:19:05.675735image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q131
median36
Q367.25
95-th percentile114
Maximum121
Range121
Interquartile range (IQR)36.25

Descriptive statistics

Standard deviation29.98508658
Coefficient of variation (CV)0.6306031635
Kurtosis0.1333836667
Mean47.54985119
Median Absolute Deviation (MAD)11
Skewness1.040233224
Sum319535
Variance899.1054175
MonotonicityNot monotonic
2021-06-02T19:19:05.987256image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
362205
32.8%
41369
 
5.5%
77355
 
5.3%
25329
 
4.9%
6270
 
4.0%
38219
 
3.3%
85209
 
3.1%
120203
 
3.0%
22197
 
2.9%
24185
 
2.8%
Other values (112)2179
32.4%
ValueCountFrequency (%)
011
 
0.2%
135
0.5%
21
 
< 0.1%
31
 
< 0.1%
42
 
< 0.1%
ValueCountFrequency (%)
12163
 
0.9%
120203
3.0%
1192
 
< 0.1%
1181
 
< 0.1%
11739
 
0.6%

start_mifid_days
Real number (ℝ≥0)

ZEROS

Distinct354
Distinct (%)5.3%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean20.47372339
Minimum0
Maximum1090
Zeros4844
Zeros (%)72.1%
Negative0
Negative (%)0.0%
Memory size52.6 KiB
2021-06-02T19:19:06.291267image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile116
Maximum1090
Range1090
Interquartile range (IQR)1

Descriptive statistics

Standard deviation82.95062164
Coefficient of variation (CV)4.051565026
Kurtosis47.63581595
Mean20.47372339
Median Absolute Deviation (MAD)0
Skewness6.278593753
Sum137522
Variance6880.80563
MonotonicityNot monotonic
2021-06-02T19:19:06.631590image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04844
72.1%
1339
 
5.0%
2140
 
2.1%
399
 
1.5%
463
 
0.9%
562
 
0.9%
757
 
0.8%
645
 
0.7%
838
 
0.6%
1131
 
0.5%
Other values (344)999
 
14.9%
ValueCountFrequency (%)
04844
72.1%
1339
 
5.0%
2140
 
2.1%
399
 
1.5%
463
 
0.9%
ValueCountFrequency (%)
10902
< 0.1%
10381
< 0.1%
9671
< 0.1%
8821
< 0.1%
8561
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size52.6 KiB
1
3818 
0
2902 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6720
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0
ValueCountFrequency (%)
13818
56.8%
02902
43.2%
2021-06-02T19:19:07.117269image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-06-02T19:19:07.269315image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
13818
56.8%
02902
43.2%

Most occurring characters

ValueCountFrequency (%)
13818
56.8%
02902
43.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6720
100.0%

Most frequent character per category

ValueCountFrequency (%)
13818
56.8%
02902
43.2%

Most occurring scripts

ValueCountFrequency (%)
Common6720
100.0%

Most frequent character per script

ValueCountFrequency (%)
13818
56.8%
02902
43.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII6720
100.0%

Most frequent character per block

ValueCountFrequency (%)
13818
56.8%
02902
43.2%

finish_mifid_days
Real number (ℝ≥0)

MISSING
ZEROS

Distinct350
Distinct (%)9.2%
Missing2900
Missing (%)43.2%
Infinite0
Infinite (%)0.0%
Mean36.01439791
Minimum0
Maximum1090
Zeros801
Zeros (%)11.9%
Negative0
Negative (%)0.0%
Memory size52.6 KiB
2021-06-02T19:19:07.483402image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q313
95-th percentile221
Maximum1090
Range1090
Interquartile range (IQR)12

Descriptive statistics

Standard deviation105.6658859
Coefficient of variation (CV)2.933990072
Kurtosis26.95379301
Mean36.01439791
Median Absolute Deviation (MAD)2
Skewness4.769749555
Sum137575
Variance11165.27945
MonotonicityNot monotonic
2021-06-02T19:19:07.808747image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1835
 
12.4%
0801
 
11.9%
2399
 
5.9%
3234
 
3.5%
4135
 
2.0%
5101
 
1.5%
676
 
1.1%
765
 
1.0%
862
 
0.9%
1052
 
0.8%
Other values (340)1060
 
15.8%
(Missing)2900
43.2%
ValueCountFrequency (%)
0801
11.9%
1835
12.4%
2399
5.9%
3234
 
3.5%
4135
 
2.0%
ValueCountFrequency (%)
10901
< 0.1%
10401
< 0.1%
9681
< 0.1%
9361
< 0.1%
8821
< 0.1%

has_deposit
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size52.6 KiB
0
4733 
1
1987 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6720
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
04733
70.4%
11987
29.6%
2021-06-02T19:19:08.320699image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-06-02T19:19:08.480699image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
04733
70.4%
11987
29.6%

Most occurring characters

ValueCountFrequency (%)
04733
70.4%
11987
29.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6720
100.0%

Most frequent character per category

ValueCountFrequency (%)
04733
70.4%
11987
29.6%

Most occurring scripts

ValueCountFrequency (%)
Common6720
100.0%

Most frequent character per script

ValueCountFrequency (%)
04733
70.4%
11987
29.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII6720
100.0%

Most frequent character per block

ValueCountFrequency (%)
04733
70.4%
11987
29.6%

first_deposit_days
Real number (ℝ≥0)

MISSING
ZEROS

Distinct314
Distinct (%)15.8%
Missing4733
Missing (%)70.4%
Infinite0
Infinite (%)0.0%
Mean60.10065425
Minimum0
Maximum1050
Zeros89
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size52.6 KiB
2021-06-02T19:19:08.673829image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median11
Q347
95-th percentile304.7
Maximum1050
Range1050
Interquartile range (IQR)43

Descriptive statistics

Standard deviation127.7414225
Coefficient of variation (CV)2.125458102
Kurtosis17.60549262
Mean60.10065425
Median Absolute Deviation (MAD)10
Skewness3.825223806
Sum119420
Variance16317.87103
MonotonicityNot monotonic
2021-06-02T19:19:08.989832image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1148
 
2.2%
2139
 
2.1%
3102
 
1.5%
598
 
1.5%
494
 
1.4%
089
 
1.3%
680
 
1.2%
868
 
1.0%
765
 
1.0%
1042
 
0.6%
Other values (304)1062
 
15.8%
(Missing)4733
70.4%
ValueCountFrequency (%)
089
1.3%
1148
2.2%
2139
2.1%
3102
1.5%
494
1.4%
ValueCountFrequency (%)
10501
< 0.1%
10421
< 0.1%
10061
< 0.1%
9841
< 0.1%
9561
< 0.1%

first_deposit_amount
Real number (ℝ≥0)

ZEROS

Distinct271
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.843212734
Minimum0
Maximum1000
Zeros4733
Zeros (%)70.4%
Negative0
Negative (%)0.0%
Memory size52.6 KiB
2021-06-02T19:19:09.298027image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.929161201
95-th percentile19.29161201
Maximum1000
Range1000
Interquartile range (IQR)1.929161201

Descriptive statistics

Standard deviation29.71117724
Coefficient of variation (CV)6.134600908
Kurtosis433.6296275
Mean4.843212734
Median Absolute Deviation (MAD)0
Skewness17.86520604
Sum32546.38957
Variance882.754053
MonotonicityNot monotonic
2021-06-02T19:19:09.723617image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04733
70.4%
1.929161201564
 
8.4%
3.858322401385
 
5.7%
7.716644803135
 
2.0%
19.29161201116
 
1.7%
38.5832240182
 
1.2%
11.574967272
 
1.1%
2.31499344160
 
0.9%
5.78748360243
 
0.6%
9.64580600426
 
0.4%
Other values (261)504
 
7.5%
ValueCountFrequency (%)
04733
70.4%
0.038583224011
 
< 0.1%
0.10224554361
 
< 0.1%
0.17362450811
 
< 0.1%
0.19291612011
 
< 0.1%
ValueCountFrequency (%)
10001
 
< 0.1%
771.66448032
 
< 0.1%
771.54873061
 
< 0.1%
462.81541791
 
< 0.1%
385.83224017
0.1%

first_deposit_platform
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.55639881
Minimum0
Maximum6
Zeros729
Zeros (%)10.8%
Negative0
Negative (%)0.0%
Memory size52.6 KiB
2021-06-02T19:19:09.984551image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.523474345
Coefficient of variation (CV)0.9788457406
Kurtosis1.278514348
Mean1.55639881
Median Absolute Deviation (MAD)0
Skewness1.639471514
Sum10459
Variance2.320974081
MonotonicityNot monotonic
2021-06-02T19:19:10.189215image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
14733
70.4%
5854
 
12.7%
0729
 
10.8%
3266
 
4.0%
669
 
1.0%
453
 
0.8%
216
 
0.2%
ValueCountFrequency (%)
0729
 
10.8%
14733
70.4%
216
 
0.2%
3266
 
4.0%
453
 
0.8%
ValueCountFrequency (%)
669
 
1.0%
5854
12.7%
453
 
0.8%
3266
 
4.0%
216
 
0.2%

mifid_actual_savings
Real number (ℝ≥0)

ZEROS

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.748958333
Minimum0
Maximum15
Zeros650
Zeros (%)9.7%
Negative0
Negative (%)0.0%
Memory size52.6 KiB
2021-06-02T19:19:10.409249image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median9
Q312
95-th percentile13
Maximum15
Range15
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.045497548
Coefficient of variation (CV)0.4623976242
Kurtosis-0.4247406397
Mean8.748958333
Median Absolute Deviation (MAD)3
Skewness-0.7451047123
Sum58793
Variance16.36605041
MonotonicityNot monotonic
2021-06-02T19:19:10.646620image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
122075
30.9%
131022
15.2%
5749
 
11.1%
0650
 
9.7%
7649
 
9.7%
6641
 
9.5%
8431
 
6.4%
9266
 
4.0%
10130
 
1.9%
1165
 
1.0%
Other values (2)42
 
0.6%
ValueCountFrequency (%)
0650
9.7%
11
 
< 0.1%
5749
11.1%
6641
9.5%
7649
9.7%
ValueCountFrequency (%)
1541
 
0.6%
131022
15.2%
122075
30.9%
1165
 
1.0%
10130
 
1.9%

mifid_next_year_savings
Real number (ℝ≥0)

ZEROS

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.356845238
Minimum0
Maximum15
Zeros650
Zeros (%)9.7%
Negative0
Negative (%)0.0%
Memory size52.6 KiB
2021-06-02T19:19:10.878622image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median8
Q312
95-th percentile13
Maximum15
Range15
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.080421732
Coefficient of variation (CV)0.4882729805
Kurtosis-0.7068966342
Mean8.356845238
Median Absolute Deviation (MAD)4
Skewness-0.4784754684
Sum56158
Variance16.64984151
MonotonicityNot monotonic
2021-06-02T19:19:11.092305image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
121416
21.1%
131267
18.9%
51004
14.9%
6821
12.2%
7717
10.7%
0650
9.7%
8406
 
6.0%
9216
 
3.2%
10109
 
1.6%
1162
 
0.9%
Other values (2)52
 
0.8%
ValueCountFrequency (%)
0650
9.7%
11
 
< 0.1%
51004
14.9%
6821
12.2%
7717
10.7%
ValueCountFrequency (%)
1551
 
0.8%
131267
18.9%
121416
21.1%
1162
 
0.9%
10109
 
1.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size52.6 KiB
0
3560 
1
3160 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6720
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0
ValueCountFrequency (%)
03560
53.0%
13160
47.0%
2021-06-02T19:19:11.596362image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-06-02T19:19:11.752709image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
03560
53.0%
13160
47.0%

Most occurring characters

ValueCountFrequency (%)
03560
53.0%
13160
47.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6720
100.0%

Most frequent character per category

ValueCountFrequency (%)
03560
53.0%
13160
47.0%

Most occurring scripts

ValueCountFrequency (%)
Common6720
100.0%

Most frequent character per script

ValueCountFrequency (%)
03560
53.0%
13160
47.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII6720
100.0%

Most frequent character per block

ValueCountFrequency (%)
03560
53.0%
13160
47.0%

mifid_experience
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size52.6 KiB
0
4602 
1
2118 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6720
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
04602
68.5%
12118
31.5%
2021-06-02T19:19:12.160264image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-06-02T19:19:12.320231image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
04602
68.5%
12118
31.5%

Most occurring characters

ValueCountFrequency (%)
04602
68.5%
12118
31.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6720
100.0%

Most frequent character per category

ValueCountFrequency (%)
04602
68.5%
12118
31.5%

Most occurring scripts

ValueCountFrequency (%)
Common6720
100.0%

Most frequent character per script

ValueCountFrequency (%)
04602
68.5%
12118
31.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII6720
100.0%

Most frequent character per block

ValueCountFrequency (%)
04602
68.5%
12118
31.5%

mifid_money_other_brokers
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size52.6 KiB
0
3593 
1
3127 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6720
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
03593
53.5%
13127
46.5%
2021-06-02T19:19:12.720723image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-06-02T19:19:12.868725image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
03593
53.5%
13127
46.5%

Most occurring characters

ValueCountFrequency (%)
03593
53.5%
13127
46.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6720
100.0%

Most frequent character per category

ValueCountFrequency (%)
03593
53.5%
13127
46.5%

Most occurring scripts

ValueCountFrequency (%)
Common6720
100.0%

Most frequent character per script

ValueCountFrequency (%)
03593
53.5%
13127
46.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII6720
100.0%

Most frequent character per block

ValueCountFrequency (%)
03593
53.5%
13127
46.5%

mifid_invested_other_brokers
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.659821429
Minimum0
Maximum15
Zeros3593
Zeros (%)53.5%
Negative0
Negative (%)0.0%
Memory size52.6 KiB
2021-06-02T19:19:13.012685image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q312
95-th percentile13
Maximum15
Range15
Interquartile range (IQR)12

Descriptive statistics

Standard deviation5.399232522
Coefficient of variation (CV)1.158677989
Kurtosis-1.521899067
Mean4.659821429
Median Absolute Deviation (MAD)0
Skewness0.49006069
Sum31314
Variance29.15171183
MonotonicityNot monotonic
2021-06-02T19:19:13.227684image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
03593
53.5%
121362
 
20.3%
13516
 
7.7%
5396
 
5.9%
6310
 
4.6%
7238
 
3.5%
8151
 
2.2%
991
 
1.4%
1028
 
0.4%
1119
 
0.3%
ValueCountFrequency (%)
03593
53.5%
5396
 
5.9%
6310
 
4.6%
7238
 
3.5%
8151
 
2.2%
ValueCountFrequency (%)
1516
 
0.2%
13516
 
7.7%
121362
20.3%
1119
 
0.3%
1028
 
0.4%

user_flow_name
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size52.6 KiB
3
3471 
0
3018 
2
 
202
1
 
29

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6720
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3
ValueCountFrequency (%)
33471
51.7%
03018
44.9%
2202
 
3.0%
129
 
0.4%
2021-06-02T19:19:13.785150image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-06-02T19:19:13.953149image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
33471
51.7%
03018
44.9%
2202
 
3.0%
129
 
0.4%

Most occurring characters

ValueCountFrequency (%)
33471
51.7%
03018
44.9%
2202
 
3.0%
129
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6720
100.0%

Most frequent character per category

ValueCountFrequency (%)
33471
51.7%
03018
44.9%
2202
 
3.0%
129
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common6720
100.0%

Most frequent character per script

ValueCountFrequency (%)
33471
51.7%
03018
44.9%
2202
 
3.0%
129
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII6720
100.0%

Most frequent character per block

ValueCountFrequency (%)
33471
51.7%
03018
44.9%
2202
 
3.0%
129
 
0.4%

first_trade_investor_account_demo_days
Real number (ℝ≥0)

MISSING
ZEROS

Distinct207
Distinct (%)6.7%
Missing3608
Missing (%)53.7%
Infinite0
Infinite (%)0.0%
Mean16.85861183
Minimum0
Maximum957
Zeros1807
Zeros (%)26.9%
Negative0
Negative (%)0.0%
Memory size52.6 KiB
2021-06-02T19:19:14.181152image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile89.45
Maximum957
Range957
Interquartile range (IQR)3

Descriptive statistics

Standard deviation67.45858591
Coefficient of variation (CV)4.001431827
Kurtosis58.23197434
Mean16.85861183
Median Absolute Deviation (MAD)0
Skewness6.7618735
Sum52464
Variance4550.660813
MonotonicityNot monotonic
2021-06-02T19:19:14.477155image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01807
26.9%
1313
 
4.7%
2202
 
3.0%
3103
 
1.5%
461
 
0.9%
560
 
0.9%
736
 
0.5%
635
 
0.5%
822
 
0.3%
1020
 
0.3%
Other values (197)453
 
6.7%
(Missing)3608
53.7%
ValueCountFrequency (%)
01807
26.9%
1313
 
4.7%
2202
 
3.0%
3103
 
1.5%
461
 
0.9%
ValueCountFrequency (%)
9571
< 0.1%
8981
< 0.1%
8521
< 0.1%
7371
< 0.1%
6911
< 0.1%

days_until_conversion_or_today
Real number (ℝ≥0)

ZEROS

Distinct1107
Distinct (%)16.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean403.1849702
Minimum0
Maximum1128
Zeros70
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size52.6 KiB
2021-06-02T19:19:14.766308image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q172.75
median339
Q3695
95-th percentile1033
Maximum1128
Range1128
Interquartile range (IQR)622.25

Descriptive statistics

Standard deviation348.2360536
Coefficient of variation (CV)0.8637128844
Kurtosis-1.012875967
Mean403.1849702
Median Absolute Deviation (MAD)294
Skewness0.5259514888
Sum2709403
Variance121268.349
MonotonicityNot monotonic
2021-06-02T19:19:15.185648image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2110
 
1.6%
1109
 
1.6%
596
 
1.4%
390
 
1.3%
487
 
1.3%
070
 
1.0%
664
 
1.0%
763
 
0.9%
958
 
0.9%
857
 
0.8%
Other values (1097)5916
88.0%
ValueCountFrequency (%)
070
1.0%
1109
1.6%
2110
1.6%
390
1.3%
487
1.3%
ValueCountFrequency (%)
11286
0.1%
11272
 
< 0.1%
11265
0.1%
11254
0.1%
11247
0.1%

is_converted
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size52.6 KiB
0
5041 
1
1679 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6720
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
05041
75.0%
11679
 
25.0%
2021-06-02T19:19:15.737494image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-06-02T19:19:15.889479image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
05041
75.0%
11679
 
25.0%

Most occurring characters

ValueCountFrequency (%)
05041
75.0%
11679
 
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6720
100.0%

Most frequent character per category

ValueCountFrequency (%)
05041
75.0%
11679
 
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common6720
100.0%

Most frequent character per script

ValueCountFrequency (%)
05041
75.0%
11679
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII6720
100.0%

Most frequent character per block

ValueCountFrequency (%)
05041
75.0%
11679
 
25.0%

Interactions

2021-06-02T19:18:30.253942image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:30.579803image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:30.856992image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:31.208995image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:31.484985image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:31.778243image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:32.072707image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:32.347755image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:32.619763image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:32.876021image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:33.152356image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:33.436349image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:33.685673image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:33.947953image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:34.194106image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:34.460397image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:34.748591image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:35.020628image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:35.282828image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:35.534825image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:35.800293image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:36.089064image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:36.401056image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:36.710405image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:36.974452image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:37.256149image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:37.556115image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:37.854789image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:38.137765image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:38.427125image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:38.712303image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:39.002394image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:39.409831image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:39.711533image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:39.970364image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:40.252724image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:40.576687image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:40.903611image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:41.195636image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:41.491609image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:41.781921image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:42.053923image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:42.307096image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:42.573820image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:42.836097image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:43.122040image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:43.449587image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:43.747324image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:44.082640image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:44.364657image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:44.624908image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:44.950181image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:45.263790image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:45.570362image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:45.863076image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:46.144987image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:46.436986image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:46.710214image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:46.994214image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:47.268457image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:47.539476image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:47.929723image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:48.201987image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:48.474024image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:48.746844image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:49.020339image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:49.319129image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:49.632351image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:49.953841image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:50.254175image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:50.522213image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:50.791165image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:51.071545image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:51.366932image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:51.656127image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:51.941308image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:52.230512image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:52.525462image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:52.804621image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:53.080636image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:53.371511image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:53.664660image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:53.956657image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:54.236654image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:54.536619image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:54.820779image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:55.111019image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:55.411019image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:55.700226image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:56.096210image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:56.368360image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:56.653599image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:56.910972image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:57.171651image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:57.426504image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:57.699272image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:57.965494image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:58.235539image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:58.507498image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:58.784750image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:59.056754image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:59.316747image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:59.564781image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:18:59.826149image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:19:00.106188image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:19:00.378186image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:19:00.643502image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:19:00.931454image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:19:01.207457image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-02T19:19:01.499457image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-06-02T19:19:16.139550image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-06-02T19:19:16.798866image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-06-02T19:19:17.474030image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-06-02T19:19:18.104756image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-06-02T19:19:18.649840image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-06-02T19:19:02.184877image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-06-02T19:19:03.156754image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-06-02T19:19:03.774158image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-06-02T19:19:04.150878image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

user_currencyuser_countrystart_mifid_dayshas_finished_mifidfinish_mifid_dayshas_depositfirst_deposit_daysfirst_deposit_amountfirst_deposit_platformmifid_actual_savingsmifid_next_year_savingsmifid_qualificationsmifid_experiencemifid_money_other_brokersmifid_invested_other_brokersuser_flow_namefirst_trade_investor_account_demo_daysdays_until_conversion_or_todayis_converted
0EUR220.00NaN0NaN0.0191200003NaN10
1USD240.00NaN0NaN0.011212000030.010
2GBP410.011.00NaN0.011213011123NaN10
3USD240.00NaN0NaN0.0168111133NaN20
4USD1141.00NaN0NaN0.0177011630.020
5USD870.010.00NaN0.01121210003NaN20
6USD600.00NaN0NaN0.015510163NaN20
7EUR360.013.00NaN0.0171310173NaN30
8EUR810.00NaN0NaN0.011212001123NaN30
9EUR360.00NaN0NaN0.018800003NaN30

Last rows

user_currencyuser_countrystart_mifid_dayshas_finished_mifidfinish_mifid_dayshas_depositfirst_deposit_daysfirst_deposit_amountfirst_deposit_platformmifid_actual_savingsmifid_next_year_savingsmifid_qualificationsmifid_experiencemifid_money_other_brokersmifid_invested_other_brokersuser_flow_namefirst_trade_investor_account_demo_daysdays_until_conversion_or_todayis_converted
6710EUR360.011.013.011.57496756131100026.071
6711EUR580.010.01199.03.8583225131310000NaN201
6712USD91811.01829.011006.01.9291610135101120NaN11280
6713EUR580.010.012.01.92916151212100000.0201
6714EUR360.00NaN0NaN0.00000010000000NaN11280
6715EUR36246.01246.01248.04.62998751212000000.02511
6716EUR380.00NaN0NaN0.000000100000000.011280
6717EUR580.010.0163.03.858322555110000.0631
6718USD20167.01169.01196.01.929161012120011200.011280
6719USD840.00NaN0NaN0.00000010000000NaN11280